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Benchmarking Machine Learning Workloads on Emerging Hardware
Tom St John · Murali Emani

Thu Sep 01 08:00 AM -- 05:00 PM (PDT) @ Mission Ballroom B4
Event URL: https://memani1.github.io/mlbench22/ »

With evolving system architectures, hardware and software stack, diverse machine learning workloads, and data, it is important to understand how these components interact with each other. Well-defined benchmarking procedures help evaluate and reason the performance gains with ML workload to system mapping.

Key problems that we seek to address are: (i) which representative ML benchmarks cater to workloads seen in industry, national labs, and interdisciplinary sciences; (ii) how to characterize the ML workloads based on their interaction with hardware; (iii) what novel aspects of hardware, such as heterogeneity in compute, memory, and bandwidth, will drive their adoption; (iv) performance modeling and projections to next-generation hardware.

The workshop will invite experts in these research areas to present recent work and potential directions to pursue. Accepted papers from a rigorous evaluation process will present state-of-the-art research efforts. We have also secured funding from MLCommonsTM to provide a best paper award for an outstanding submission. A panel discussion will foster an interactive platform for discussion between speakers and the audience.

Author Information

Tom St John (Cruise)
Murali Emani (Argonne National Laboratory)

Murali Emani is an Assistant Computer Scientist in the Data Science group with the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. His research interests are at the intersection of systems and machine learning including Parallel programming models, Hardware accelerators for ML/DL, High Performance Computing, Scalable Machine Learning, Runtime Systems, Performance optimization, Emerging HPC architectures, Online Adaptation. Prior, he was a Postdoctoral Research Staff Member at Lawrence Livermore National Laboratory, US. Murali obtained his PhD and worked as a Research Associate at the Institute for Computing Systems Architecture at the School of Informatics, University of Edinburgh, UK. His research resulted in multiple publications at top conferences such as PACT, PLDI and granted patents. Murali served as technical program committee member for conferences including ICPP'19, CCGRID'19, PACT '18, CCGRID '18, ICPP '18. He chaired the first Birds-of-feather session on Machine Learning benchmarking on HPC systems at Supercomputing 2019.

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